Smoothing Spline Estimator in Nonparametric Regression (Application: Poverty in Papua Province)

DOI

10.2991/assehr.k.210305.044How to use a DOI?

Keywords

Smoothing Spline, Poverty in Papua Province

Abstract

Three estimates were obtained in estimating the regression curve, namely estimation of parametric regression, nonparametric regression and semiparametric regression. The most popular nonparametric regression option is smoothing spline. The advantage of smoothing spline is that it can use variable data at certain sub intervals, so this model needs to find its own data estimation. Smoothing Spline allows characters to function smoothly. In everyday life, data patterns are often found to change at certain sub-intervals, one of which is poverty data in Papua Province. Papua Province is ranked first in the percentage of poor people in Indonesia. The best nonparametric Smoothing Spline regression model for the poverty model in Papua Province with a generalized cross validation (GCV) value of 92.77 and R2=99.99%.

Copyright

© 2021, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Ni P. A. M. Mariati
AU  - I N. Budiantara
AU  - Vita Ratnasari
PY  - 2021
DA  - 2021/03/08
TI  - Smoothing Spline Estimator in Nonparametric Regression (Application: Poverty in Papua Province)
BT  - Proceedings of the 7th International Conference on Research, Implementation, and Education of Mathematics and Sciences (ICRIEMS 2020)
PB  - Atlantis Press
SP  - 309
EP  - 314
SN  - 2352-5398
UR  - https://doi.org/10.2991/assehr.k.210305.044
DO  - 10.2991/assehr.k.210305.044
ID  - Mariati2021
ER  -